Science Score: 67.0%
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: plos.org -
○Committers with academic emails
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.6%) to scientific vocabulary
Repository
PCM toolbox - implementation in Python
Basic Info
- Host: GitHub
- Owner: DiedrichsenLab
- License: other
- Language: Jupyter Notebook
- Default Branch: master
- Size: 10.4 MB
Statistics
- Stars: 14
- Watchers: 6
- Forks: 1
- Open Issues: 0
- Releases: 4
Metadata Files
README.md
Pattern Component Modelling toolbox (Python)
Pattern component modeling (PCM) is a likelihood approach for evaluating representational models - models that specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Similar to encoding models, PCM evaluates the ability of models to predict novel brain activity patterns. In contrast to encoding models, however, the activity of individual voxels across conditions (activity profiles) is not directly fitted. Rather, PCM integrates over all possible activity profiles and computes the marginal likelihood of the data under the activity profile distribution specified by the representational model. By using an analytical expression for the marginal likelihood, PCM allows the fitting of flexible representational models, in which the relative strength and form of different feature sets can be estimated from the data.
This is a repository for the Python version. For a MATLAB verion of this toolbox see here.
Documentation
Full documentation can be found here
Licence and Acknowledgements
The PCMPy toolbox is being developed by members of the Diedrichsenlab including Jörn Diedrichsen, Giacomo Ariani, Spencer Arbuckle, Eva Berlot, and Atsushi Yokoi. It is distributed under MIT License, meaning that it can be freely used and re-used, as long as proper attribution in form of acknowledgments and links (for online use) or citations (in publications) are given. The relevant references are:
- Diedrichsen, J., Yokoi, A., & Arbuckle, S. A. (2018). Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns. Neuroimage. 180(Pt A), 119-133. [link]
- Diedrichsen, J., Ridgway, G., Friston, K.J., Wiestler, T., (2011). Comparing the similarity and spatial structure of neural representations: A pattern-component model. Neuroimage. [link]
For more theoretical background:
- Diedrichsen, J. (2018). Representational models and the feature fallacy. In M. S. Gazzaniga (Ed.), The Cognitive Neurosciences. [link]
- Diedrichsen, J., & Kriegeskorte, N. (2017). Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS Comput Biol. [link]
Owner
- Name: Diedrichsen Lab
- Login: DiedrichsenLab
- Kind: organization
- Email: joern.diedrichsen@googlemail.com
- Location: Western University
- Website: http://www.diedrichsenlab.org/index.html
- Repositories: 11
- Profile: https://github.com/DiedrichsenLab
Citation (CITATION.cff)
cff-version: 1.2.0 message: "Please cite this software as below." authors: - family-names: "Diedrichsen" given-names: "Jörn" orcid: "https://orcid.org/0000-0003-0264-8532" title: "Pattern Component Modelling Toolbox" version: 1.0.0 date-released: 2023-07-20 url: "https://github.com/DiedrichsenLab/PcmPy"
GitHub Events
Total
- Watch event: 2
- Push event: 8
- Pull request event: 2
- Fork event: 1
- Create event: 1
Last Year
- Watch event: 2
- Push event: 8
- Pull request event: 2
- Fork event: 1
- Create event: 1
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 163
- Total Committers: 4
- Avg Commits per committer: 40.75
- Development Distribution Score (DDS): 0.092
Top Committers
| Name | Commits | |
|---|---|---|
| Jörn Diedrichsen | j****n@g****m | 148 |
| Giacomo Ariani | g****i@g****m | 9 |
| spike7697 | 7****7@u****m | 4 |
| Spencer Arbuckle | s****e@g****m | 2 |
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 1
- Total pull requests: 5
- Average time to close issues: 11 days
- Average time to close pull requests: 2 months
- Total issue authors: 1
- Total pull request authors: 4
- Average comments per issue: 1.0
- Average comments per pull request: 0.4
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: about 1 month
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- mehrdadkashefi (1)
Pull Request Authors
- mshahbazi1997 (2)
- nshervt (2)
- dwadh (1)
- g14r (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 22 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 2
- Total maintainers: 1
pypi.org: pcmpy
Pattern Component Modeling of multivariate activity patterns
- Homepage: https://github.com/DiedrichsenLab/PCMPy
- Documentation: https://pcmpy.readthedocs.io/
- License: MIT
-
Latest release: 1.1.0
published over 1 year ago
Rankings
Maintainers (1)
Dependencies
- ipykernel *
- matplotlib *
- nbsphinx *
- numpy *
- pandas *
- scipy *
- seaborn *
- sphinx >=1.4
- sphinx-copybutton *
- sphinxcontrib-bibtex *
- matplotlib *
- numpy *
- pandas *
- scipy *
- seaborn *